A system and method for generating synthetic streamflow data for a malfunctioning streamgage is provided. The method uses both classification and regression techniques to accurately predict streamflow data for the malfunctioning streamgage based on measured streamflow data from other streamgages and based on correlations between the streamgages. The system and method may also provide a method of improved flood forecasting, by updating flood forecasts using synthetic streamflow data when measured streamflow data from one or more streamgages are unavailable. The system may generate flood forecast information and/or flood warning messages.
Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for improving the accuracy of generating synthetic streamflow data if there is an interruption in measured streamflow data from a target streamflow source, the method comprising: receiving measured streamflow data related to a plurality of streamflow sources, the plurality of streamflow sources including the target streamflow source and at least one other streamflow source disposed in a different location from the target streamflow source; clustering the plurality of streamflow sources, using the measured streamflow data, into two or more groups of streamflow sources based on correlations between streamflow sources in the plurality of streamflow sources; and using information about the two or more groups of streamflow sources to generate synthetic streamflow data for the target streamflow source if there is an interruption in measured streamflow data from the target streamflow source.
The invention relates to improving the accuracy of synthetic streamflow data generation when measured data from a target streamflow source is interrupted. Streamflow data is essential for water resource management, but gaps in measurements can occur due to sensor failures or other disruptions. The method addresses this by leveraging data from multiple streamflow sources to maintain accurate synthetic data during interruptions. The method involves receiving measured streamflow data from multiple sources, including the target source and at least one other source located in a different area. These sources are clustered into groups based on correlations in their streamflow patterns. The clustering identifies relationships between sources, allowing the system to use data from correlated sources to estimate the target streamflow when direct measurements are unavailable. This approach ensures continuity and accuracy in synthetic streamflow data, even during interruptions, by leveraging spatial and temporal relationships between nearby or similarly behaving streamflow sources. The method improves reliability in water management systems by reducing errors in synthetic data generation.
2. The method according to claim 1 , wherein the streamflow data includes water level surface height.
A method for analyzing streamflow data in hydrological monitoring systems addresses the challenge of accurately measuring and predicting water flow in rivers and streams. The method involves collecting and processing streamflow data, which includes water level surface height measurements. This data is used to determine the volume and velocity of water flow, enabling real-time monitoring and forecasting of stream conditions. By incorporating water level surface height, the method improves the accuracy of streamflow calculations, particularly in dynamic environments where water levels fluctuate due to rainfall, runoff, or other factors. The technique may also integrate additional data sources, such as precipitation records or riverbed topography, to enhance predictive models. This approach supports applications in flood management, water resource planning, and environmental monitoring, providing reliable insights for decision-making in water-related infrastructure and ecological studies. The method ensures precise and timely data collection, reducing uncertainties in hydrological assessments and improving the reliability of water flow predictions.
3. The method according to claim 2 , wherein the method includes determining that a measurement device configured to provide water level surface height data from the target streamflow source is malfunctioning.
This invention relates to monitoring water level surface height in streamflow sources using measurement devices. The problem addressed is ensuring accurate and reliable data collection when a measurement device malfunctions. The method involves detecting and handling malfunctions in a measurement device that provides water level surface height data from a target streamflow source. The device is monitored to determine if it is malfunctioning, which may involve checking for errors, inconsistencies, or deviations from expected performance. If a malfunction is detected, corrective actions can be taken, such as triggering an alert, switching to a backup device, or applying a correction algorithm to compensate for the malfunction. This ensures continuous and reliable water level data collection, which is critical for applications like flood monitoring, water resource management, and environmental studies. The method may also include preprocessing the collected data to improve accuracy before analysis or reporting. The overall system may integrate multiple measurement devices and data processing components to enhance robustness and reliability in water level monitoring.
4. The method according to claim 1 , wherein the target streamflow source is located along a first stream and wherein the at least one other streamflow source is located along a second stream, wherein the second stream is different from the first stream.
This invention relates to water resource management, specifically methods for optimizing streamflow distribution between multiple water sources. The problem addressed is the efficient allocation of water from different streamflow sources to meet target flow requirements while minimizing waste and ensuring sustainable usage. The method involves selecting a target streamflow source located along a first stream and at least one additional streamflow source located along a second, distinct stream. The system determines the optimal flow rates from each source to meet predefined target flow conditions, such as maintaining ecological balance or supporting downstream water needs. By integrating data from multiple streams, the method dynamically adjusts water distribution to account for variations in flow rates, environmental conditions, or demand fluctuations. The approach includes analyzing real-time or historical streamflow data to identify the most effective sources for water extraction or redirection. It may also incorporate predictive modeling to anticipate future flow changes and adjust allocations accordingly. The system ensures that water is sourced from the most sustainable locations while preventing over-extraction from any single stream. This method is particularly useful in regions with multiple interconnected waterways where balancing water usage across different streams is critical for environmental and agricultural sustainability. The solution enhances water management efficiency by leveraging data-driven decision-making to optimize resource allocation.
5. The method according to claim 1 , wherein the method includes building a predictive model from the measured streamflow data related to the plurality of streamflow sources and from the information about the two or more groups of streamflow sources.
This invention relates to water resource management, specifically to methods for analyzing and predicting streamflow data from multiple sources. The problem addressed is the challenge of accurately modeling and forecasting streamflow in complex river systems where water originates from diverse sources, such as tributaries, groundwater, and precipitation. Traditional methods often fail to account for the interactions between different streamflow sources, leading to inaccurate predictions. The method involves collecting measured streamflow data from a plurality of streamflow sources, such as rivers, tributaries, and groundwater inflows. The data is analyzed to identify two or more distinct groups of streamflow sources based on their characteristics, such as flow patterns, seasonal variations, or geographic proximity. A predictive model is then built using the measured streamflow data and the identified groups. The model incorporates the relationships between the groups to improve the accuracy of streamflow predictions. This approach allows for better water resource planning, flood forecasting, and drought management by accounting for the dynamic interactions between different water sources. The method can be applied in hydrological studies, environmental monitoring, and infrastructure management to enhance decision-making in water-related applications.
6. The method according to claim 1 , wherein the method includes: receiving streamflow data transmitted by a measurement device at the target streamflow source; determining that the measurement device has ceased to transmit streamflow data; and predicting streamflow data associated with the measurement device after determining that the measurement device has ceased to transmit streamflow data.
This invention relates to water resource management, specifically addressing the challenge of maintaining accurate streamflow data when measurement devices fail or stop transmitting. Streamflow data is critical for flood prediction, water supply management, and environmental monitoring, but gaps in data due to device malfunctions can lead to inaccurate assessments. The invention provides a method to predict missing streamflow data when a measurement device at a target streamflow source stops transmitting. The method involves receiving streamflow data from the device, detecting when transmission ceases, and then predicting future streamflow data based on historical patterns, environmental conditions, or other correlated data sources. The prediction may use statistical models, machine learning, or hydrological simulations to estimate the missing values. This ensures continuity in data collection, improving decision-making for water resource management and disaster response. The invention enhances reliability in monitoring systems by compensating for device failures without requiring immediate physical repairs or replacements.
7. A method for improving the accuracy of generating synthetic streamflow data if there is an interruption in measured streamflow data related to a target streamflow source, the method comprising: receiving measured streamflow data related to a plurality of streamflow sources, the plurality of streamflow sources including the target streamflow source; receiving clustering information about the plurality of streamflow sources; creating a predictive model using the measured streamflow data and the clustering information; using the predictive model to generate synthetic streamflow data for the target streamflow source if an interruption in measured streamflow data from the target streamflow source has been determined; wherein the predictive model is trained using the measured streamflow data and the clustering information; and wherein the predictive model is created using an ensemble of decision trees.
This invention addresses the challenge of maintaining accurate synthetic streamflow data when measured data from a target streamflow source is interrupted. Streamflow data is critical for water resource management, but gaps in measurements can occur due to sensor failures or other disruptions. The method improves accuracy by leveraging data from multiple streamflow sources and clustering information to generate reliable synthetic data during interruptions. The method involves receiving measured streamflow data from multiple sources, including the target source experiencing interruptions. Clustering information, which categorizes the streamflow sources based on similarities in their flow patterns or geographic proximity, is also received. This data is used to train a predictive model, specifically an ensemble of decision trees, which combines multiple decision tree models to enhance prediction accuracy. The trained model generates synthetic streamflow data for the target source when interruptions are detected, ensuring continuous and reliable data availability. By incorporating clustering information, the model accounts for relationships between different streamflow sources, improving the accuracy of synthetic data during gaps in measurements. This approach is particularly useful in hydrological monitoring and water management systems where uninterrupted data is essential.
8. The method according to claim 7 , wherein the target streamflow source is a target streamgage and wherein the plurality of streamflow sources is a plurality of streamgages.
The invention relates to hydrological monitoring and streamflow prediction, addressing the challenge of accurately estimating streamflow at a target location using data from multiple streamflow sources. The method involves selecting a target streamgage, which is a monitoring station that measures water flow in a river or stream, and a plurality of streamgages that provide upstream or nearby streamflow data. The method processes data from these streamgages to generate a predictive model for the target streamgage. This model accounts for spatial and temporal variations in streamflow, improving accuracy compared to relying on a single source. The method may also incorporate additional data, such as precipitation or historical records, to refine predictions. By leveraging multiple streamgages, the system enhances reliability in water resource management, flood forecasting, and environmental monitoring. The approach is particularly useful in regions with sparse or inconsistent data, where traditional single-source methods may fail. The invention ensures robust streamflow estimation by dynamically adjusting the model based on real-time or historical streamgage inputs.
9. The method according to claim 7 , wherein the streamflow sources in the plurality of streamflow sources are separated geographically.
This invention relates to a method for managing and analyzing streamflow data from multiple sources to improve water resource monitoring and prediction. The method addresses the challenge of integrating diverse streamflow data to enhance accuracy in hydrological modeling and flood forecasting. Streamflow data is collected from a plurality of streamflow sources, which are geographically separated, meaning they are located in different regions or watersheds. The method involves processing this data to account for spatial variations in streamflow patterns, ensuring that the data reflects local hydrological conditions. By analyzing the geographically separated streamflow sources, the method provides a more comprehensive understanding of water flow dynamics across different areas, improving the reliability of predictions for water management, flood control, and environmental monitoring. The method may also include steps to validate and calibrate the data to ensure consistency and accuracy, particularly when integrating data from different geographic locations. This approach enhances the ability to detect regional trends, assess water availability, and respond to hydrological events more effectively.
10. The method according to claim 7 , wherein at least two streamflow sources in the plurality of streamflow sources are located along different streams.
This invention relates to a method for monitoring and analyzing streamflow data from multiple sources to improve water resource management. The method addresses the challenge of accurately assessing water availability and distribution across different streams, which is critical for flood prediction, drought monitoring, and sustainable water use. The method involves collecting streamflow data from a plurality of streamflow sources, where at least two of these sources are located along different streams. This ensures that the data represents diverse hydrological conditions, improving the reliability of the analysis. The collected data is then processed to generate insights, such as flow rates, water volume, and temporal variations. By integrating data from multiple streams, the method provides a comprehensive understanding of water dynamics across a region, enabling better decision-making for water management and infrastructure planning. The method may also include steps for data validation, error correction, and real-time monitoring to enhance accuracy. This approach is particularly useful in regions with complex river networks or where water resources are shared among multiple users.
11. The method according to claim 7 , wherein the predictive model is a random forest model.
A predictive modeling system is designed to analyze data and generate predictions based on input features. The system addresses the challenge of accurately forecasting outcomes in complex datasets where relationships between variables are non-linear or difficult to interpret. Traditional linear models often fail to capture these intricate patterns, leading to suboptimal predictions. The system employs a random forest model, an ensemble learning technique that constructs multiple decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees. This approach improves accuracy and robustness by reducing overfitting and variance compared to single decision trees. The random forest model is trained on a dataset containing input features and corresponding target values. During operation, the system processes new input data through the trained model to generate predictions. The random forest's ability to handle high-dimensional data, missing values, and non-linear relationships makes it particularly effective for tasks such as classification, regression, and anomaly detection in various domains, including finance, healthcare, and manufacturing. The system may also include preprocessing steps to clean and normalize the input data before feeding it into the model, ensuring optimal performance.
12. The method according to claim 7 , wherein the measured streamflow data and the synthetic streamflow data have the same temporal resolutions.
This invention relates to hydrological data processing, specifically methods for comparing measured and synthetic streamflow data to improve water resource management. The problem addressed is the difficulty in accurately assessing streamflow data due to discrepancies in temporal resolution between measured and synthetic datasets, which can lead to unreliable analyses and decision-making. The method involves generating synthetic streamflow data using a hydrological model, which simulates streamflow based on environmental factors such as precipitation, evaporation, and land use. The synthetic data is then compared to measured streamflow data obtained from sensors or gauges in the same river or watershed. A key aspect of the method is ensuring that both datasets have identical temporal resolutions, meaning they are sampled at the same time intervals (e.g., hourly, daily, or monthly). This alignment allows for direct, meaningful comparisons between the two datasets. By maintaining consistent temporal resolution, the method improves the accuracy of streamflow analysis, enabling better detection of anomalies, calibration of hydrological models, and prediction of future water availability. This approach is particularly useful for water resource managers, engineers, and researchers who rely on streamflow data for flood forecasting, drought monitoring, and infrastructure planning. The method can be applied to various water bodies, including rivers, streams, and reservoirs, and is adaptable to different climatic and geographic conditions.
13. A method for improving the accuracy of flood forecasting, the method comprising: receiving measured streamflow data from a plurality of streamgages, the plurality of streamgages including a target streamgage and at least one other streamgage disposed in a different location from the target streamgage; clustering the plurality of streamgages, using the measured streamflow data, into two or more groups of streamgages based on correlations between streamgages in the plurality of streamgages; building a predictive model, using the two or more groups of streamgages, to predict synthetic streamflow data for any streamgage in the plurality of streamgages; generating flood forecast information using the measured streamflow data; determining that there has been an interruption in the receiving of streamflow data from the target streamgage; retrieving the predictive model; using the predictive model to generate synthetic streamflow data for the target streamgage; and updating the flood forecast information using the synthetic streamflow data.
Flood forecasting accuracy is critical for early warning systems, but interruptions in streamflow data from key monitoring locations can degrade predictions. This invention addresses the problem by using a predictive modeling approach to maintain accurate flood forecasts even when direct measurements are unavailable. The method involves collecting streamflow data from multiple streamgages, including a primary target location and others in different areas. The streamgages are grouped based on statistical correlations in their flow patterns, creating clusters of related monitoring points. A predictive model is then trained using these clusters to estimate synthetic streamflow data for any streamgage in the network. When data from the target streamgage is interrupted, the system retrieves the predictive model to generate synthetic flow estimates, which are integrated into the flood forecast. This ensures continuous and reliable flood predictions by leveraging indirect relationships between streamgages when direct measurements are missing. The approach improves resilience in flood monitoring systems by reducing dependency on uninterrupted data streams.
14. The method according to claim 13 , wherein the predictive model receives as input at least some of the measured streamflow data from the plurality of streamgages.
This invention relates to predictive modeling for streamflow data, addressing the challenge of accurately forecasting water flow in rivers and streams. The method involves using a predictive model that processes streamflow measurements from multiple streamgages to generate predictions. The predictive model is trained using historical streamflow data, which includes measurements from the same or similar streamgages, to establish relationships between input variables and streamflow outcomes. The model may incorporate additional data, such as weather forecasts, to improve accuracy. During operation, the model receives real-time or near-real-time streamflow measurements from the streamgages and uses these inputs to predict future streamflow conditions. The predictions can be used for flood forecasting, water resource management, or other hydrological applications. The method ensures that the predictive model remains updated with the latest streamflow data, enhancing its reliability and adaptability to changing conditions. The system may also include validation steps to assess the model's performance and adjust its parameters as needed. This approach improves the accuracy and timeliness of streamflow predictions, supporting better decision-making in water management and disaster response.
15. The method according to claim 13 , wherein the method further includes generating a flood warning message according to the updated flood forecast information.
This invention relates to flood forecasting and warning systems. The problem addressed is the need for accurate and timely flood warnings to mitigate risks to communities and infrastructure. The method involves generating flood forecast information based on real-time data, such as rainfall, river levels, and terrain data. The system updates the flood forecast dynamically as new data is received, improving prediction accuracy. Additionally, the method includes generating a flood warning message based on the updated forecast information. This warning can be disseminated to authorities, emergency services, or the public to enable proactive measures. The system may also incorporate historical flood data and predictive models to enhance forecast reliability. The goal is to provide early and precise flood alerts, reducing response times and potential damage. The method ensures continuous monitoring and adjustment of flood predictions, making it suitable for real-time applications in flood-prone regions.
16. The method according to claim 13 , wherein the predictive model comprises a decision tree model.
A predictive model system is used to analyze data and generate predictions based on input variables. The system addresses the challenge of accurately forecasting outcomes in complex datasets where relationships between variables are non-linear or difficult to model with traditional statistical methods. The predictive model is trained using historical data to learn patterns and relationships, then applied to new data to make predictions. The model includes a decision tree structure, which recursively splits data into subsets based on input variable thresholds, creating a tree-like decision path. Each split is determined by an algorithm that evaluates which variable and threshold best separate the data into groups with more homogeneous outcomes. The decision tree model is particularly effective for handling categorical and numerical data, and it provides interpretable results by visually representing decision rules. The system may also include preprocessing steps to clean and transform input data, as well as post-processing steps to refine predictions. The decision tree model can be used in various applications, such as risk assessment, customer segmentation, or anomaly detection, where understanding the underlying decision logic is valuable. The method ensures that the model is trained efficiently and accurately, with the decision tree structure allowing for both high predictive performance and transparency in decision-making.
17. The method according to claim 13 , wherein building the predictive model includes a step of training the predictive model using measured streamflow data from the plurality of streamgages including measured streamflow data from the target streamgage.
This invention relates to hydrological modeling, specifically to improving the accuracy of predictive models for streamflow forecasting. The problem addressed is the challenge of accurately predicting streamflow at a target location (target streamgage) when historical data from that location is limited or unavailable. The solution involves building a predictive model that leverages streamflow data from multiple nearby streamgages (plurality of streamgages) to enhance forecasting accuracy. The method includes training a predictive model using measured streamflow data from these multiple streamgages, including data from the target streamgage itself. This approach allows the model to incorporate spatial and temporal relationships between different streamgage locations, improving its ability to predict streamflow at the target location. The training process may involve machine learning techniques, statistical methods, or other data-driven approaches to identify patterns and dependencies in the streamflow data. By integrating data from multiple sources, the model can compensate for gaps or inconsistencies in the target streamgage's historical data, leading to more reliable and accurate predictions. This method is particularly useful in regions where streamflow data is sparse or where real-time monitoring is limited. The resulting predictive model can be used for flood forecasting, water resource management, and other hydrological applications.
18. The method according to claim 13 , wherein the measured streamflow data and the synthetic streamflow data have the same temporal resolutions.
This invention relates to hydrological data analysis, specifically methods for comparing measured and synthetic streamflow data to improve water resource management. The problem addressed is the difficulty in accurately assessing streamflow variability due to discrepancies in data resolution between observed measurements and model-generated synthetic data. The invention provides a method where both measured and synthetic streamflow data are processed to ensure they share identical temporal resolutions before comparison. This alignment allows for more precise analysis of streamflow patterns, enabling better calibration of hydrological models and more reliable predictions of water availability. The method involves adjusting the temporal resolution of either the measured or synthetic data through interpolation or aggregation to match the other dataset. This ensures that any differences in streamflow characteristics are attributable to actual hydrological variations rather than artifacts of differing data resolutions. The invention is particularly useful in scenarios where high-resolution synthetic data is compared with lower-resolution measured data, or vice versa, to support decision-making in water resource planning and flood risk assessment. By standardizing temporal resolution, the method enhances the accuracy of streamflow data analysis, leading to improved model performance and more informed water management strategies.
19. The method according to claim 13 , wherein the plurality of streamgages are associated with a geographic region, and wherein the step of generating flood forecast information includes using remote sensing data associated with the geographic region.
This invention relates to flood forecasting systems that utilize streamgage data and remote sensing data to improve accuracy and coverage. The method involves collecting real-time water level measurements from multiple streamgages deployed across a geographic region. These measurements are processed to generate flood forecast information, which includes predictions of water levels, flow rates, and potential flood risks. To enhance the forecasting accuracy, the method incorporates remote sensing data, such as satellite imagery or aerial observations, that corresponds to the same geographic region where the streamgages are located. The remote sensing data provides additional environmental context, such as precipitation patterns, land surface conditions, and water body extents, which are integrated with the streamgage data to refine the flood forecasts. This approach allows for more reliable and spatially comprehensive flood predictions, particularly in areas where streamgage coverage is limited or where rapid changes in hydrological conditions occur. The system may also include data validation steps to ensure the accuracy of the remote sensing inputs before they are used in the forecasting model. By combining ground-based measurements with remote observations, the method aims to provide timely and actionable flood warnings to support disaster preparedness and response efforts.
20. The method according to claim 13 , wherein the flood forecast information comprises a real-time flood map.
A system and method for flood forecasting and management provides real-time flood monitoring and predictive analytics to mitigate flood risks. The technology addresses the challenge of accurately predicting and visualizing flood events to enable timely decision-making and emergency response. The system collects and processes data from various sources, including weather forecasts, hydrological models, and sensor networks, to generate flood forecasts. These forecasts are then used to create dynamic flood maps that display current and predicted flood extents in real-time. The real-time flood map visually represents flood-prone areas, water depth, and flood progression, allowing authorities and individuals to assess risks and take preventive measures. The system may also integrate with emergency response systems to trigger automated alerts and resource allocation based on the flood forecast data. By providing actionable insights through real-time visualization, the technology enhances flood preparedness and reduces the impact of flooding events.
21. A system for improving the accuracy of flood forecasting, comprising: a streamflow prediction system configured to: receive measured streamflow data from a plurality of streamgages, the plurality of streamgages including a target streamgage and at least one other streamgage disposed in a different location from the target streamgage; cluster the plurality of streamgages, using the measured streamflow data, into two or more groups of streamgages based on correlations between streamgages in the plurality of streamgages; build a predictive model, using the two or more groups of streamgages, to predict synthetic streamflow data for any streamgage in the plurality of streamgages; a flood forecasting system configured to: generate flood forecast information using the measured streamflow data; determine that there has been an interruption in the receiving of streamflow data from the target streamgage; retrieve the predictive model from the streamflow prediction system; use the predictive model to generate synthetic streamflow data for the target streamgage; and update the flood forecast information using the synthetic streamflow data.
This system improves flood forecasting accuracy by addressing data interruptions from streamgages. The system includes a streamflow prediction component that receives real-time streamflow measurements from multiple streamgages, including a primary target streamgage and others located in different areas. It clusters these streamgages into groups based on flow correlations, then builds predictive models to estimate synthetic streamflow data for any streamgage in the network. A separate flood forecasting component generates flood predictions using the measured data. If the target streamgage's data is interrupted, the system retrieves the predictive model, generates synthetic data for the target streamgage, and updates the flood forecast with this synthetic data. This ensures continuous and accurate flood predictions even when direct measurements are unavailable. The approach leverages spatial correlations between streamgages to maintain forecasting reliability during data gaps.
22. The system according to claim 21 , wherein the predictive model receives as input at least some of the measured streamflow data from the plurality of streamgages.
A system for monitoring and predicting streamflow conditions uses a predictive model that processes real-time data from multiple streamgages. These streamgages measure water flow rates at various locations within a river or watershed. The system collects this measured streamflow data and feeds at least some of it into the predictive model. The model analyzes the data to forecast future streamflow conditions, such as potential flooding or drought events. By integrating data from multiple streamgages, the system improves accuracy in predicting water flow dynamics across different sections of the river system. This helps water resource managers, emergency responders, and environmental agencies make informed decisions to mitigate risks and optimize water management strategies. The predictive model may also incorporate additional factors like rainfall, temperature, or historical data to enhance forecasting precision. The system supports real-time monitoring and proactive response to changing streamflow conditions, ensuring better preparedness for natural water-related events.
23. The method according to claim 21 , wherein the flood forecasting system is further configured to generate a flood warning message according to the updated flood forecast information.
A flood forecasting system monitors and predicts flood events by analyzing real-time and historical hydrological data, such as rainfall, river levels, and soil moisture. The system processes this data using computational models to generate flood forecasts, which are then updated dynamically as new data is received. To enhance situational awareness, the system is configured to generate flood warning messages based on the updated forecast information. These warnings may include alerts about potential flood risks, severity levels, and recommended actions for affected areas. The system may also integrate with communication networks to disseminate these warnings to authorities, emergency services, and the public. By providing timely and accurate flood warnings, the system helps mitigate risks, improve preparedness, and reduce the impact of flooding events. The flood forecasting system may also incorporate additional features, such as data validation, uncertainty analysis, and integration with other environmental monitoring systems, to ensure the reliability and accuracy of the forecasts.
24. The method according to claim 21 , wherein the predictive model comprises a decision tree model.
A predictive model is used to analyze data and generate predictions based on input variables. The model is trained on historical data to identify patterns and relationships that can be applied to new, unseen data. A key challenge in predictive modeling is selecting an appropriate algorithm that balances accuracy, interpretability, and computational efficiency. Decision tree models are a type of predictive model that use a tree-like structure to make decisions based on input features. Each node in the tree represents a decision rule, and branches represent possible outcomes, leading to a final prediction at the leaf nodes. Decision trees are popular due to their simplicity, ability to handle both numerical and categorical data, and interpretability, as the decision-making process is visually represented in the tree structure. However, they can be prone to overfitting if not properly regularized. The use of a decision tree model in a predictive system allows for transparent and explainable predictions, making it suitable for applications where understanding the reasoning behind predictions is important. The model can be trained using various algorithms, such as CART (Classification and Regression Trees) or ID3, and can be optimized through techniques like pruning or ensemble methods to improve performance.
25. The method according to claim 21 , wherein the streamflow prediction system is further configured to build the predictive model by training the predictive model using measured streamflow data from the plurality of streamgages including measured streamflow data from the target streamgage.
A system and method for predicting streamflow in a target streamgage using a predictive model trained with measured streamflow data from multiple streamgages, including the target streamgage. The system addresses the challenge of accurately forecasting streamflow in a specific location by leveraging data from surrounding streamgages to improve prediction accuracy. The predictive model is trained using historical streamflow measurements from the target streamgage and neighboring streamgages, allowing it to account for spatial and temporal variations in water flow. This approach enhances the reliability of streamflow predictions by incorporating a broader dataset, which helps mitigate errors caused by localized anomalies or insufficient data at the target location. The system may use machine learning or statistical techniques to build the model, ensuring it adapts to changing environmental conditions and historical patterns. By integrating data from multiple sources, the system provides more robust and accurate streamflow forecasts, which are critical for water resource management, flood prediction, and environmental monitoring. The method ensures that the predictive model is continuously refined with real-world data, improving its performance over time.
26. The method according to claim 21 , wherein the measured streamflow data and the synthetic streamflow data have the same temporal resolutions.
This invention relates to hydrological data processing, specifically methods for comparing measured and synthetic streamflow data to improve water resource management. The problem addressed is the difficulty in accurately validating synthetic streamflow data generated by models against real-world measurements due to mismatched temporal resolutions, which can lead to unreliable comparisons and decision-making. The method involves aligning measured streamflow data with synthetic streamflow data by ensuring both datasets share identical temporal resolutions. This synchronization allows for direct, meaningful comparisons between observed and modeled streamflow values. The process includes obtaining measured streamflow data from sensors or gauges and synthetic streamflow data from computational models. The temporal resolution of both datasets is standardized, meaning they are aggregated or interpolated to the same time intervals (e.g., hourly, daily, or monthly). This alignment enables accurate statistical analysis, trend detection, and model validation, improving the reliability of water resource assessments. The method may also involve preprocessing steps like noise reduction or gap-filling to enhance data quality before comparison. By ensuring consistent temporal resolution, the approach enhances the accuracy of hydrological modeling and supports better-informed water management decisions.
27. The method according to claim 21 , wherein the plurality of streamgages are associated with a geographic region, and wherein the flood forecasting system is configured to use remote sensing data associated with the geographic region.
The invention relates to flood forecasting systems that utilize streamgage data and remote sensing data to predict flooding events. The system monitors water levels at multiple streamgages located within a specific geographic region. These streamgages provide real-time or near-real-time measurements of water flow and levels in rivers, streams, or other water bodies. The system integrates this streamgage data with remote sensing data, such as satellite imagery or aerial observations, to enhance the accuracy of flood predictions. Remote sensing data may include precipitation measurements, soil moisture levels, land cover information, or other environmental factors that influence flooding. By combining streamgage measurements with remote sensing data, the system improves flood forecasting by accounting for broader environmental conditions beyond localized water level readings. This approach allows for more reliable and timely flood warnings, helping communities prepare for potential flooding events. The system may also adjust its forecasting models based on the integrated data to refine predictions over time. The invention is particularly useful in regions prone to flooding, where accurate and early warnings are critical for public safety and infrastructure protection.
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June 22, 2018
February 1, 2022
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